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Answer: Random Forest
Random Forest stands out as the optimal choice for managing outliers and ensuring robust predictions in regression tasks within Spark ML. This algorithm operates by constructing multiple decision trees and merging their predictions, a method known for its resilience and effectiveness in mitigating the influence of outliers. The collective decision-making process inherent to Random Forest significantly diminishes the outliers' impact on the model's overall performance.
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When tackling a regression problem, your team is faced with the challenge of effectively managing outliers. Which Spark ML algorithm is capable of dealing with outliers and delivering robust predictions even when extreme values are present?
A
Linear Regression
B
Random Forest
C
Lasso Regression
D
Decision Trees